Prognostic accuracy of MALDI-TOF mass spectrometric analysis of plasma in COVID-19

Lazari, Lucas Cardoso; Ghilardi, Fabio De Rose; Rosa-Fernandes, Livia; Assis, Diego M; Nicolau, José Carlos; Santiago, Veronica Feijoli; Dalçóquio, Talia Falcão; Angeli, Claudia B; Bertolin, Adriadne Justi; Marinho, Claudio RF; Wrenger, Carsten; Durigon, Edison Luiz; Siciliano, Rinaldo Focaccia; Palmisano, Giuseppe

SARS-CoV-2 infection poses a global health crisis. In parallel with the ongoing world effort to identify therapeutic solutions, there is a critical need for improvement in the prognosis of COVID-19. Here, we report plasma proteome fingerprinting that predict high (hospitalized) and low-risk (outpatients) cases of COVID-19 identified by a platform that combines machine learning with matrix-assisted laser desorption ionization mass spectrometry analysis. Sample preparation, MS, and data analysis parameters were optimized to achieve an overall accuracy of 92%, sensitivity of 93%, and specificity of 92% in dataset without feature selection. We identified two distinct regions in the MALDI-TOF profile belonging to the same proteoforms. A combination of SDS–PAGE and quantitative bottom-up proteomic analysis allowed the identification of intact and truncated forms of serum amyloid A-1 and A-2 proteins, both already described as biomarkers for viral infections in the acute phase. Unbiased discrimination of high- and low-risk COVID-19 patients using a technology that is currently in clinical use may have a prompt application in the noninvasive prognosis of COVID-19. Further validation will consolidate its clinical utility.